# Textually Enriched Neural Module Networks for Visual Question Answering Source: [Textually Enriched Neural Module Networks for Visual Question Answering]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:12:58.577Z Authors: Khyathi Raghavi Chandu, Mary Arpita Pyreddy, Matthieu Felix, Narendra Nath Joshi Published: 2018-09-23T23:45:54.000Z ## Abstract Problems at the intersection of language and vision, like visual question answering, have recently been gaining a lot of attention in the field of multi-modal machine learning as computer vision research moves beyond traditional recognition tasks. There has been recent success in visual question answering using deep neural network models which use the linguistic structure of the questions to dynamically instantiate network layouts. In the process of converting the question to a network layout, the question is simplified, which results in loss of information in the model. In this paper, we enrich the image information with textual data using image captions and external knowledge bases to generate more coherent answers. We achieve 57.1% overall accuracy on the test-dev open-ended questions from the visual question answering (VQA 1.0) real image dataset. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()